(b) Credit card fraud dataset. We use the real-world credit card (CC) fraud dataset [1]} containing European credit card transactions such that most variables are transformed using PCA to yield 28 principal components as features and an `Amount' variable denoting the amount transacted. We select the first 4 principal components to create a 4-D dataset, and separate the dataset into 5 classes according to Amount percentiles so as to simulate collaborating banks serving different populations that tend to make transactions within certain ranges of amounts. Synthetic data are obtained by sampling from a distribution fit to the CC dataset with kernel density estimation.
